--- base_model: - mlabonne/ChimeraLlama-3-8B-v3 - johnsnowlabs/JSL-MedLlama-3-8B-v2.0 library_name: transformers tags: - mergekit - merge license: llama3 --- # Chimera_MedLlama-3-8B This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [mlabonne/ChimeraLlama-3-8B-v3](https://huggingface.co/mlabonne/ChimeraLlama-3-8B-v3) * [johnsnowlabs/JSL-MedLlama-3-8B-v2.0](https://huggingface.co/johnsnowlabs/JSL-MedLlama-3-8B-v2.0) ### Evaluation - multimedqa (0 shot)
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr| |-------------------------------|-------|------|-----:|--------|-----:|---|-----:| | - medmcqa |Yaml |none | 0|acc |0.6087|± |0.0075| | | |none | 0|acc_norm|0.6087|± |0.0075| | - medqa_4options |Yaml |none | 0|acc |0.6269|± |0.0136| | | |none | 0|acc_norm|0.6269|± |0.0136| | - anatomy (mmlu) | 0|none | 0|acc |0.6963|± |0.0397| | - clinical_knowledge (mmlu) | 0|none | 0|acc |0.7585|± |0.0263| | - college_biology (mmlu) | 0|none | 0|acc |0.7847|± |0.0344| | - college_medicine (mmlu) | 0|none | 0|acc |0.6936|± |0.0351| | - medical_genetics (mmlu) | 0|none | 0|acc |0.8200|± |0.0386| | - professional_medicine (mmlu)| 0|none | 0|acc |0.7684|± |0.0256| |stem |N/A |none | 0|acc_norm|0.6129|± |0.0066| | | |none | 0|acc |0.6440|± |0.0057| | - pubmedqa | 1|none | 0|acc |0.7480|± |0.0194| |Groups|Version|Filter|n-shot| Metric |Value | |Stderr| |------|-------|------|-----:|--------|-----:|---|-----:| |stem |N/A |none | 0|acc_norm|0.6129|± |0.0066| | | |none | 0|acc |0.6440|± |0.0057| ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: mlabonne/ChimeraLlama-3-8B-v3 layer_range: [0, 32] - model: johnsnowlabs/JSL-MedLlama-3-8B-v2.0 layer_range: [0, 32] merge_method: slerp base_model: mlabonne/ChimeraLlama-3-8B-v3 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```